Metadata-Version: 2.4
Name: triples-sigfast
Version: 0.3.1
Summary: High-performance, JIT-compiled time-series and signal processing core.
Home-page: https://github.com/TripleS-Studio/sigfast
Author: TripleS Studio
Author-email: golamsamdani301416@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.20.0
Requires-Dist: numba>=0.55.0
Requires-Dist: pandas>=1.3.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

#  SigFast

![PyPI](https://img.shields.io/badge/PyPI-v0.3.1-blue)
![License](https://img.shields.io/badge/License-MIT-green)

A high-performance time-series processing library built for Data Scientists and Physicists. Uses **Numba JIT** and **C-level multithreading** to bypass the Python GIL.

### Why SigFast?
Pandas is great, but it runs on a single thread. When analyzing millions of data points (IoT sensors, high-frequency trading, astrophysics), Pandas becomes a bottleneck. SigFast distributes the math across all your CPU cores.

**Benchmark (10 Million Data Points - Rolling Window):**
*    Pandas `.rolling().mean()`: **~1.20 seconds**
*    SigFast Engine: **~0.03 seconds (40x Faster)**

### Installation
```bash
pip install sigfast
